In [6]:
from typing import Optional
from collections import Counter
import plotly
import plotly.graph_objects as go
import plotly.express as px
from PIL import Image
import io
from IPython.display import display
def plotly_figure_to_image(fig, extension: str = 'png', **kwargs):
img_bytes = fig.to_image(format=extension, **kwargs)
return Image.open(io.BytesIO(img_bytes))
def save_image(image, name: str, save_dir: str = "images", extension: str="png"):
if not os.path.exists(save_dir):
os.mkdir(save_dir)
filename = "".join(i for i in name if i not in "\\/:*?<>| ")
filepath = f"{save_dir}/{filename}.{extension}"
image.save(filepath)
def stackedBarChartDF(
df: pd.DataFrame,
title: str,
palette: list,
attributes: list = ["Understandability", "Usability", "Informativeness", "Value"],
labels: Optional[list] = None,
save_fig: bool = False,
save_dir: str = "images",
web_display: bool = False
):
counters = [Counter(df[attribute]) for attribute in attributes]
fig = go.Figure() # type: ignore
category_order = [
"Strongly disagree",
"Disagree",
"Slightly disagree",
"Neutral",
"Slightly agree",
"Agree",
"Strongly agree",
]
number_order = [1, 2, 3, 4, 5, 6, 7]
labels = labels if labels else attributes
def add_bar_trace(num, transform, showlegend: bool = True):
rating = number_order[num]
counts = [counter[rating] for counter in counters]
total_count = sum(counters[0].values())
xvals = [transform(count) / total_count for count in counts]
fig.add_trace(
go.Bar(
x=xvals,
y=labels,
orientation="h",
name=category_order[num],
width=0.8,
marker_color=palette[num],
legendgroup=category_order[num],
showlegend=False
) # type: ignore
)
# negative side
add_bar_trace(3, lambda x: x * -0.5)
for num in reversed(range(0, 3)):
add_bar_trace(num, lambda x: x * -1)
# positive side
add_bar_trace(3, lambda x: x * 0.5, showlegend=False)
for num in range(4, 7):
add_bar_trace(num, lambda x: x)
for num in range(0, 7):
fig.add_trace(
go.Bar(
x=[0] * 7,
y=labels,
orientation="h",
name=category_order[num],
marker_color=palette[num],
legendgroup=category_order[num]
) # type: ignore
)
fig.update_layout(
barmode="relative",
yaxis_autorange="reversed",
title=dict(
text=title,
x=0.5,
y=0.9
),
xaxis={"tick0": 0, "title": "Frequency", "tickformat": "%", "range": [-1, 1]},
legend=dict(
orientation="h",
yanchor="bottom",
y=-0.4,
xanchor="center",
x=0.5,
),
)
im = plotly_figure_to_image(fig, width=1400, height=400, validate=True)
if web_display:
fig.show()
else:
display(im)
if save_fig:
save_image(im, name, save_dir)
return fig, counters
palette = [plotly.colors.diverging.RdBu[n] for n in [2, 3, 4, 5, 6, 7, 8]] # type: ignore
space = " " * 4
labels = [
f"I find the explanation{space}<br>intuitively understandable{space}",
f"The explanation helps me to understand{space}<br>factors relevant to the algorithm{space}",
f"The explanation helps me to decide whether{space}<br> I can trust the generated annotations{space}",
f"The explanation provides me with{space}<br>valuable information for my work{space}",
]
counters = []
for name, df in result_dataframes.items():
fig, counter = stackedBarChartDF(
df,
name,
palette,
labels=labels,
web_display=False,
save_fig=False,
save_dir=figure_save_dir,
)
px.imshow(df.corr(method='spearman'), zmin=0, width = 500, height=300).show()
for comment in comments[name]:
print(comment)
counters.append(counter)
Like previous, initial reaction is that interpolation is useful but doesn't give the full picture of possible negatives. Perhaps clicking around each cell and seeing the interpolation for each would feel more useful What is a counterfactual?
It seems there may be many counterfactual examples that are more closely related to the positive example which could be useful for understanding the nuances and building trust Do not understand this question
Per-cell saliency is probably too much detail.
There is one cell slightly to the right of the center that is labeled as positive and is quite darkly stained by IHC, but does not show up on the saliency map, which is a bit surprising. It looks like an endothelial cell.
This is excellent but I think there could be quite a lot of additional factors, so this would need some supervision.
High confidence should provide examples of both classes (pos. and neg.), no? Confidence for what? positive or negative or both? I felt I needed the pos or neg labels in addition to the confidence